Surface Temperature#
Highlights
The Pacific Islands mean temperature over land increased by 1.1°C (2°F) since 1951, whereas in Palau, the records show an increase of 0.53 °C from 1951 to 2025.
On the other hand, the minimum temperatures show an increasing trend of 0.013 °C/year and the maximum temperatures a non signiticative trend of 0.001, making the variability of temperatures within each day to be decreasing at -0.01 °C/year.
The top 10 years on record have occurred since 2001.
Show code cell source
import warnings
warnings.filterwarnings("ignore")
Show code cell source
import os.path as op
import sys
import contextlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from myst_nb import glue
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, fig_int_to_glue
from ind_setup.plotting import plot_bar_probs, fontsize
from ind_setup.tables import get_data_metrics, plot_df_table
sys.path.append("../../../functions")
from data_downloaders import GHCN
Define location and variables of interest#
country = 'Palau'
vars_interest = ['TMIN', 'TMAX']
Get Data#
update_data = False
path_data = "../../../data"
Using Koror Station#
Analysis of how much the maximum and minimum temperatures over time are changing.
The analysis of the difference between these 2 variables will allow us to know how the daily variability is being modified
https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf
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if update_data:
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
Show code cell source
if update_data:
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
id = 'PSW00040309' # Koror Station
dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
st_data = st_data.resample('Y').mean()
glue("n_years", len(np.unique(st_data.index.year)), display=False)
glue("start_year", st_data.dropna().index[0].year, display=False)
glue("end_year", st_data.dropna().index[-1].year, display=False)
Mean temperature#
dict_plot = [{'data' : st_data, 'var' : 'TMEAN', 'ax' : 1, 'label' : 'TMEAN'},
]
dict_plot = [{'data' : st_data, 'var' : 'TMEAN', 'ax' : 1, 'label' : 'TMEAN'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12))
glue("trend_fig_mean", fig_int_to_glue(fig), display=False)
Fig. Annual maxima corresponding to the mean temperature.
st_data['TMEAN_ref'] = st_data['TMEAN'] - st_data.loc['1961':'1990'].TMEAN.mean()
# import matplotlib.pyplot as plt
# plot_bar_probs(x = st_data.index.year, y = st_data.TMEAN_ref, trendline = True, figsize = [15, 4])
# plt.title('Temperature anomalies (Over and above 1961 - 1990 reference period)', fontsize = 15);
nevents = 10
top_10 = st_data.sort_values(by='TMEAN_ref', ascending=False).head(nevents)
from ind_setup.tables import plot_df_table
var = 'TMEAN'
df = get_data_metrics(st_data, var, )
fig = plot_df_table(df.T, figsize = (300, 400))
st_data
| TMIN | TMAX | diff | TMEAN | TMEAN_ref | |
|---|---|---|---|---|---|
| DATE | |||||
| 1951-12-31 | 24.150543 | 30.924457 | 6.773913 | 27.537500 | -0.069683 |
| 1952-12-31 | 23.837978 | 30.904645 | 7.066667 | 27.371311 | -0.235871 |
| 1953-12-31 | 24.088767 | 31.039726 | 6.950959 | 27.564247 | -0.042936 |
| 1954-12-31 | 25.000548 | 31.084384 | 6.083836 | 28.042466 | 0.435283 |
| 1955-12-31 | 23.756986 | 30.741918 | 6.984932 | 27.249452 | -0.357731 |
| ... | ... | ... | ... | ... | ... |
| 2021-12-31 | 24.745042 | 28.618414 | 3.873371 | 26.681728 | -0.925455 |
| 2022-12-31 | 24.541781 | 29.917466 | 5.375685 | 27.229623 | -0.377560 |
| 2023-12-31 | 25.518440 | 30.079787 | 4.561348 | 27.799113 | 0.191931 |
| 2024-12-31 | 25.843614 | 30.157944 | 4.314330 | 28.000779 | 0.393596 |
| 2025-12-31 | 25.641176 | 30.170588 | 4.529412 | 27.905882 | 0.298700 |
75 rows × 5 columns
fig, ax, trend = plot_bar_probs(x=st_data.index.year, y=st_data.TMEAN_ref, trendline=True,
y_label='Mean Temperature [°C]', figsize=[15, 4], return_trend=True)
glue("trend_mean", float(trend), display=False)
glue("change_mean", float(trend * len(np.unique(st_data.index.year))), display=False)
glue("top_10_year", float(top_10.sort_index().index.year[0]), display=False)
im = ax.scatter(top_10.index.year, top_10.TMEAN_ref,
c=top_10.TMEAN_ref.values, s=100, cmap='rainbow', label='Top 10 warmest years')
plt.title('Temperature anomalies (Over and above 1961 - 1990 reference period)', fontsize=15)
plt.colorbar(im).set_label('Mean Temperature [°C]', fontsize=fontsize)
glue("trend_fig", fig, display=False)
Fig. 1 Annomaly of the mean temperature over and above the 1961-1990 reference period. Overlapping points correspond to the top 10 warmer years.#
Minimum and Maximum Temperatures#
dict_plot = [{'data' : st_data, 'var' : 'TMIN', 'ax' : 1, 'label' : 'TMIN'},
{'data' : st_data, 'var' : 'TMAX', 'ax' : 2, 'label' : 'TMAX'},
# {'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}
]
fig, TRENDS = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (24, 11), return_trend = True)
fig.write_html(op.join(path_data, 'figures', 'F3_ST_min_max.html'), include_plotlyjs="cdn")
glue("trend_min", float(TRENDS[0]), display=False)
glue("trend_max", float(TRENDS[1]), display=False)
glue("change_min", float(TRENDS[0]*len(np.unique(st_data.index.year))), display=False)
glue("change_max", float(TRENDS[1]*len(np.unique(st_data.index.year))), display=False)
glue("trend_fig_max_min", fig_int_to_glue(fig), display=False)
Fig. Annual maximum (red) and minimum (blue) temperature at Koror. The solid black line represents the trend, which is statistically significant (p < 0.05). The dashed black line represents a trend that is not statistically significant.
var = 'TMIN'
df = get_data_metrics(st_data, var, )
fig = plot_df_table(df.T, figsize = (300, 400))
var = 'TMAX'
df = get_data_metrics(st_data, var, )
fig = plot_df_table(df.T, figsize = (300, 400))
Difference temperature#
dict_plot = [{'data' : st_data, 'var' : 'diff', 'ax' : 1, 'label' : 'Difference TMAX - TMIN'}]
fig, trend = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12), return_trend = True)
glue("trend_diff", float(trend[0]), display=False)
Fig. Annual maximum of the difference of the maximum and minimum temperature within each day
var = 'diff'
df = get_data_metrics(st_data, var, )
fig = plot_df_table(df.T, figsize = (300, 400))